Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine Learning Algorithms for Classifying Land Use and Land Cover
https://orcid.org/http://orcid.org/0000-0003-4415-5998
https://orcid.org/http://orcid.org/0000-0003-0089-4385
We are in the Big Data era, the quantity of geospatial data gathered or stored using remotely sensed satellite imagery for land-use and land-cover (LULC) mapping and secondary geospatial data files grow. Innovative cloud computing, deep learning methods, and machine learning have also recently been developed. Deep learning algorithms got a lot of interest because of their enhanced performance in separation, category, and supplementary machine algorithm applications. Land use and land cover (LULC) are key elements of a broad range of ecological uses in remote sensing. Land-use changes occur on the geographical and spatial scale owing to the precision, development capabilities, flexibility, uncertainties, structure, and ability to incorporate existing patterns. As a result, the high performance of LULC modelling necessitates the use of a broad range of pattern modes in remote sensing, including dynamical, statistic, and deep learning models. Advances in remote sensing technology, and hence the rapidly increasing amount of timely data accessible on a worldwide scale, open new possibilities for a variety of applications. This article provides a summary of the basic ML and DL ideas that apply to the LULC is explained, including their pros and cons. To address the difficult issue of identifying changes in LULC, the application of deep learning to land usage, and this review has clarified both their advantages and disadvantages as researched by various scholars.
Machine Learning Algorithms for Classifying Land Use and Land Cover
https://orcid.org/http://orcid.org/0000-0003-4415-5998
https://orcid.org/http://orcid.org/0000-0003-0089-4385
We are in the Big Data era, the quantity of geospatial data gathered or stored using remotely sensed satellite imagery for land-use and land-cover (LULC) mapping and secondary geospatial data files grow. Innovative cloud computing, deep learning methods, and machine learning have also recently been developed. Deep learning algorithms got a lot of interest because of their enhanced performance in separation, category, and supplementary machine algorithm applications. Land use and land cover (LULC) are key elements of a broad range of ecological uses in remote sensing. Land-use changes occur on the geographical and spatial scale owing to the precision, development capabilities, flexibility, uncertainties, structure, and ability to incorporate existing patterns. As a result, the high performance of LULC modelling necessitates the use of a broad range of pattern modes in remote sensing, including dynamical, statistic, and deep learning models. Advances in remote sensing technology, and hence the rapidly increasing amount of timely data accessible on a worldwide scale, open new possibilities for a variety of applications. This article provides a summary of the basic ML and DL ideas that apply to the LULC is explained, including their pros and cons. To address the difficult issue of identifying changes in LULC, the application of deep learning to land usage, and this review has clarified both their advantages and disadvantages as researched by various scholars.
Machine Learning Algorithms for Classifying Land Use and Land Cover
https://orcid.org/http://orcid.org/0000-0003-4415-5998
https://orcid.org/http://orcid.org/0000-0003-0089-4385
We are in the Big Data era, the quantity of geospatial data gathered or stored using remotely sensed satellite imagery for land-use and land-cover (LULC) mapping and secondary geospatial data files grow. Innovative cloud computing, deep learning methods, and machine learning have also recently been developed. Deep learning algorithms got a lot of interest because of their enhanced performance in separation, category, and supplementary machine algorithm applications. Land use and land cover (LULC) are key elements of a broad range of ecological uses in remote sensing. Land-use changes occur on the geographical and spatial scale owing to the precision, development capabilities, flexibility, uncertainties, structure, and ability to incorporate existing patterns. As a result, the high performance of LULC modelling necessitates the use of a broad range of pattern modes in remote sensing, including dynamical, statistic, and deep learning models. Advances in remote sensing technology, and hence the rapidly increasing amount of timely data accessible on a worldwide scale, open new possibilities for a variety of applications. This article provides a summary of the basic ML and DL ideas that apply to the LULC is explained, including their pros and cons. To address the difficult issue of identifying changes in LULC, the application of deep learning to land usage, and this review has clarified both their advantages and disadvantages as researched by various scholars.
Machine Learning Algorithms for Classifying Land Use and Land Cover
Lecture Notes in Civil Engineering
Sreekeshava, K. S. (Herausgeber:in) / Kolathayar, Sreevalsa (Herausgeber:in) / Vinod Chandra Menon, N. (Herausgeber:in) / Asha Rani, N. R. (Autor:in) / Inayathulla, M. (Autor:in)
International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development ; 2023
26.03.2024
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Mapping land-cover modifications over large areas: A comparison of machine learning algorithms
| Online Contents | 2008